DFT Convergence Tips
Practical strategies for achieving convergence in challenging DFT calculations, including mixing schemes and initial guess optimization.
Read MoreSharing Knowledge & Life
Welcome to my blog where I share technical insights from my research and glimpses of life beyond the lab. From DFT calculations to table tennis strategies, this is where science meets personal interests.
Technical Tips & Tutorials
Getting Started with ML Force Fields
A beginner's guide to implementing machine learning force fields using ASE and MACE for molecular dynamics simulations.
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Useful Quantum ESPRESSO Scripts
Collection of Python scripts for automating Quantum ESPRESSO workflows, from input generation to data analysis.
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Beautiful Plots for Materials Science
Creating publication-quality figures for band structures, density of states, and crystal structures using matplotlib and plotly.
Read More2025 Yi Cao. This work is licensed under CC BY-NC-SA 4.0.
Life Beyond Research
My Table Tennis Journey
From beginner to competitive player - lessons learned on and off the table, and how sports complement research life.
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Learning Tennis at 24
Why I decided to pick up tennis during my PhD and the surprising parallels between mastering a sport and research.
Read More
Four Seasons at Hopkins
Capturing the beauty of JHU's Homewood campus through the seasons - a photographer's perspective.
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Conference Cities Through My Lens
Exploring DC, Boston, Bellevue, and beyond - combining conference attendance with street photography.
View GalleryQuick Code Snippets
Recent Snippets
Parse QE `relax.out` for energies
import re
from ase.io import read
# Read the last structure from Quantum ESPRESSO output
atoms = read('relax.out') # Automatically reads the last image
# Total energy in eV
energy_eV = atoms.get_potential_energy()
# Lattice parameters
cell = atoms.get_cell()
a, b, c = cell.lengths() # in Ångstrom
alpha, beta, gamma = cell.angles() # in degrees
# Print results
print(f"Total Energy: {energy_eV:.6f} eV")
print(f"Lattice Parameters (a, b, c): {a:.6f}, {b:.6f}, {c:.6f} Å")
print(f"Lattice Angles (α, β, γ): {alpha:.2f}°, {beta:.2f}°, {gamma:.2f}°")
return energiesPosted: January 10, 2025 | View on GitHub
Quick band gap extraction
import numpy as np
def extract_bandgap_from_qe_output(filename='bands.out'):
with open(filename, 'r') as f:
lines = f.readlines()
fermi_energy = None
band_energies = []
kpoints = []
current_kpoint_bands = []
for line in lines:
if 'the Fermi energy is' in line:
fermi_energy = float(line.split()[-2]) # in eV
elif 'k =' in line:
if current_kpoint_bands:
band_energies.append(current_kpoint_bands)
current_kpoint_bands = []
k_line = line.strip().split()
kpt = list(map(float, [k_line[2], k_line[3], k_line[4]]))
kpoints.append(kpt)
elif line.strip() and len(line.split()) == 2:
band_energy = float(line.split()[1])
current_kpoint_bands.append(band_energy)
if current_kpoint_bands:
band_energies.append(current_kpoint_bands)
# Convert to numpy array: shape (n_kpoints, n_bands)
band_energies = np.array(band_energies)
if fermi_energy is None:
raise ValueError("Fermi energy not found in file.")
# Shift energies relative to Fermi level
shifted_energies = band_energies - fermi_energy
# Find VBM and CBM
vbm = np.max(shifted_energies[shifted_energies <= 0])
cbm = np.min(shifted_energies[shifted_energies >= 0])
band_gap = cbm - vbm if cbm > vbm else 0.0
print(f"Fermi energy: {fermi_energy:.6f} eV")
print(f"Valence Band Maximum (VBM): {vbm:.6f} eV")
print(f"Conduction Band Minimum (CBM): {cbm:.6f} eV")
print(f"Band Gap: {band_gap:.6f} eV")
if band_gap == 0.0:
print("This material is metallic.")
else:
# Check direct vs indirect band gap
vbm_k = np.where(shifted_energies == vbm)[0][0]
cbm_k = np.where(shifted_energies == cbm)[0][0]
if vbm_k == cbm_k:
print("Direct band gap.")
else:
print("Indirect band gap.")
# Run it
extract_bandgap_from_qe_output('bands.out')
Posted: January 8, 2025 | Tags: Quantum ESPRESSO, ASE, band structure
2025 Yi Cao. This work is licensed under MIT License.
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